INTUITION-1: TOWARD IN-ORBIT BARE SOIL DETECTION USING SPECTRAL VEGETATION INDICES

被引:0
|
作者
Wijata, Agata M. [1 ,2 ]
Lakota, Tomasz [1 ]
Cwiek, Marcin [1 ]
Ruszczak, Bogdan [1 ,3 ]
Gumiela, Michal [1 ]
Tulczyjew, Lukasz [1 ]
Bartoszek, Andrzej [1 ]
Longepe, Nicolas [4 ]
Smykala, Krzysztof [5 ]
Nalepa, Jakub [1 ,2 ]
机构
[1] KP Labs, Bojkowska 37J, PL-44100 Gliwice, Poland
[2] Silesian Tech Univ, Akad 2A, PL-44100 Gliwice, Poland
[3] Opole Univ Technol, Proszkowska 76, PL-45758 Opole, Poland
[4] European Space Agcy, Largo Galileo Galilei 1, I-00044 Frascati, Italy
[5] QZ Solut, Ozimska 72A, PL-45310 Opole, Poland
来源
IGARSS 2024-2024 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, IGARSS 2024 | 2024年
关键词
Bare soil; hyperspectral images; on-board processing; vegetation indices; remote sensing; big data;
D O I
10.1109/IGARSS53475.2024.10640702
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Bare soil detection is an important step in soil composition analysis, as it can prune the areas that should be excluded from more expensive processing aimed at extracting selected soil parameters from hyperspectral images acquired in orbit. This is of paramount importance for on-board applications, where hardware constraints of an edge device (a satellite), such as computational and memory requirements or energy consumption need to be considered while processing big data in space. In this paper, we present a simple yet effective bare soil detection algorithm exploiting vegetation indices that is ready for in-orbit deployment. Our experimental study performed over the airborne hyperspectral data shows that this approach can be robustly used for simulated bands, i.e., wide bands aggregating several narrow neighboring bands within the spectrum. Therefore, we can apply our technique to sensors with lower spectral resolution. Finally, it offers high-quality bare soil delineation reaching the Dice Index of 0.85.
引用
收藏
页码:1708 / 1712
页数:5
相关论文
共 16 条
  • [11] Detection and Quantification of Vegetation Losses with Sentinel-2 Images Using Bi-Temporal Analysis of Spectral Indices and Transferable Random Forest Model
    Rynkiewicz, Alicja
    Hoscilo, Agata
    Aune-Lundberg, Linda
    Nilsen, Anne B.
    Lewandowska, Aneta
    REMOTE SENSING, 2025, 17 (06)
  • [12] Estimating the Agricultural Farm Soil Moisture Using Spectral Indices of Landsat 8, and Sentinel-1, and Artificial Neural Networks
    Ghasemloo, Nima
    Matkan, Ali Akbar
    Alimohammadi, Abbas
    Aghighi, Hossein
    Mirbagheri, Babak
    JOURNAL OF GEOVISUALIZATION AND SPATIAL ANALYSIS, 2022, 6 (02)
  • [13] Estimating the Agricultural Farm Soil Moisture Using Spectral Indices of Landsat 8, and Sentinel-1, and Artificial Neural Networks
    Nima Ghasemloo
    Ali Akbar Matkan
    Abbas Alimohammadi
    Hossein Aghighi
    Babak Mirbagheri
    Journal of Geovisualization and Spatial Analysis, 2022, 6
  • [14] Agricultural burned area detection using an integrated approach utilizing multi spectral instrument based fire and vegetation indices from Sentinel-2 satellite
    Deshpande, Monish Vijay
    Pillai, Dhanyalekshmi
    Jain, Meha
    METHODSX, 2022, 9
  • [15] Possibility of Zhuhai-1 Hyperspectral Imagery for Monitoring Salinized Soil Moisture Content Using Fractional Order Differentially Optimized Spectral Indices
    Kahaer, Yasenjiang
    Tashpolat, Nigara
    Shi, Qingdong
    Liu, Suhong
    WATER, 2020, 12 (12)
  • [16] High Spatial and Temporal Soil Moisture Retrieval in Agricultural Areas Using Multi-Orbit and Vegetation Adapted Sentinel-1 SAR Time Series
    Mengen, David
    Jagdhuber, Thomas
    Balenzano, Anna
    Mattia, Francesco
    Vereecken, Harry
    Montzka, Carsten
    REMOTE SENSING, 2023, 15 (09)